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Found 18 papers, 8 papers with code
Multimodal Negotiation Corpus with Various Subjective Assessments for Social-Psychological Outcome Prediction from Non-Verbal Cues
To investigate SPNOP, a corpus with various psychological measurements is beneficial because the interaction process of negotiation relates to many aspects of psychology.
ToMATO: Verbalizing the Mental States of Role-Playing LLMs for Benchmarking Theory of Mind
These verbalized thoughts serve as answers to questions designed to assess the mental states of characters within conversations.
End-to-End Joint Target and Non-Target Speakers ASR
Target-speaker ASR systems are a promising way to only transcribe a target speaker's speech by enrolling the target speaker's information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss
In this work, we propose a new SE training criterion that minimizes the distance between clean and enhanced signals in the feature representation of the SSL model to alleviate the mismatch.
MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames
With FIF, we apply a temporal mask to the input mel-spectrogram and encourage the converter to fill in missing frames based on surrounding frames.
Model architectures to extrapolate emotional expressions in DNN-based text-to-speech
In this study, the meaning of "extrapolate emotional expressions" is to borrow emotional expressions from others, and the collection of emotional speech uttered by target speakers is unnecessary.
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion
To address this, we examined the applicability of CycleGAN-VC/VC2 to mel-spectrogram conversion.
Nonparallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks
We previously proposed a method that allows for nonparallel voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN.
Many-to-Many Voice Transformer Network
The main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers.
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
To bridge this gap, we rethink conditional methods of StarGAN-VC, which are key components for achieving non-parallel multi-domain VC in a single model, and propose an improved variant called StarGAN-VC2.
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data.
WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation
WaveCycleGAN has recently been proposed to bridge the gap between natural and synthesized speech waveforms in statistical parametric speech synthesis and provides fast inference with a moving average model rather than an autoregressive model and high-quality speech synthesis with the adversarial training.
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms
This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks.
ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion
Second, it achieves many-to-many conversion by simultaneously learning mappings among multiple speakers using only a single model instead of separately learning mappings between each speaker pair using a different model.
WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks
The experimental results demonstrate that our proposed method can 1) alleviate the over-smoothing effect of the acoustic features despite the direct modification method used for the waveform and 2) greatly improve the naturalness of the generated speech sounds.
ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder
Such situations can be avoided by introducing an auxiliary classifier and training the encoder and decoder so that the attribute classes of the decoder outputs are correctly predicted by the classifier.
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.
Generative adversarial network-based approach to signal reconstruction from magnitude spectrograms
In this paper, we address the problem of reconstructing a time-domain signal (or a phase spectrogram) solely from a magnitude spectrogram.
